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<dc:title xml:lang="pl"><![CDATA[Neural network segmentation of images from stained cucurbits leaves with colour symptoms of biotic and abiotic stresses]]></dc:title>
<dc:creator><![CDATA[Gocławski, Jarosław]]></dc:creator>
<dc:creator><![CDATA[Sekulska-Nalewajko, Joanna]]></dc:creator>
<dc:creator><![CDATA[Kuźniak, Elżbieta]]></dc:creator>
<dc:subject xml:lang="pl"><![CDATA[image segmentation]]></dc:subject>
<dc:subject xml:lang="pl"><![CDATA[colour space]]></dc:subject>
<dc:subject xml:lang="pl"><![CDATA[morphological processing]]></dc:subject>
<dc:subject xml:lang="pl"><![CDATA[image thresholding]]></dc:subject>
<dc:subject xml:lang="pl"><![CDATA[artificial neural networks]]></dc:subject>
<dc:subject xml:lang="pl"><![CDATA[WTA learning]]></dc:subject>
<dc:subject xml:lang="pl"><![CDATA[Widrow?Hoff learning]]></dc:subject>
<dc:subject xml:lang="pl"><![CDATA["Cucurbita" species]]></dc:subject>
<dc:subject xml:lang="pl"><![CDATA[plant stress]]></dc:subject>
<dc:subject xml:lang="pl"><![CDATA[ROS detection]]></dc:subject>
<dc:description xml:lang="pl"><![CDATA[The increased production of Reactive Oxygen Species (ROS) in plant leaf tissues is a hallmark of a plant?s reaction to various environmental stresses. This paper describes an automatic segmentation method for scanned images of cucurbits leaves stained to visualise ROS accumulation sites featured by specific colour hues and intensities. The leaves placed separately in the scanner view field on a colour background are extracted by thresholding in the RGB colour space, then cleaned from petioles to obtain a leaf blade mask.]]></dc:description>
<dc:description xml:lang="pl"><![CDATA[The second stage of the method consists in the classification of within mask pixels in a hue-saturation plane using two classes, determined by leaf regions with and without colour products of the ROS reaction. At this stage a two-layer, hybrid artificial neural network is applied with the first layer as a self-organising Kohonen type network and a linear perceptron output layer (counter propagation network type).]]></dc:description>
<dc:description xml:lang="pl"><![CDATA[The WTA-based, fast competitive learning of the first layer was improved to increase clustering reliability. Widrow?Hoff supervised training used at the output layer utilises manually labelled patterns prepared from training images. The generalisation ability of the network model has been verified by K-fold cross-validation. The method significantly accelerates the measurement of leaf regions containing the ROS reaction colour products and improves measurement accuracy.]]></dc:description>
<dc:publisher><![CDATA[Zielona Góra: Uniwersytet Zielonogórski]]></dc:publisher>
<dc:contributor><![CDATA[Korbicz, Józef (1951- ) - red.]]></dc:contributor>
<dc:contributor><![CDATA[Uciński, Dariusz - red.]]></dc:contributor>
<dc:date><![CDATA[2012]]></dc:date>
<dc:type xml:lang="pl"><![CDATA[artykuł]]></dc:type>
<dc:identifier><![CDATA[http://www.zbc.uz.zgora.pl/repozytorium/Content/47017/AMCS_2012_22_3_13.pdf]]></dc:identifier>
<dc:identifier><![CDATA[https://zbc.uz.zgora.pl/repozytorium/dlibra/publication/55128/edition/47017/content]]></dc:identifier>
<dc:identifier><![CDATA[oai:zbc.uz.zgora.pl:47017]]></dc:identifier>
<dc:source xml:lang="pl"><![CDATA[AMCS, Volume 22, Number 3 (2012)]]></dc:source>
<dc:source xml:lang="pl"><![CDATA[https://www.amcs.uz.zgora.pl/?action=paper&paper=641]]></dc:source>
<dc:language><![CDATA[eng]]></dc:language>
<dc:relation><![CDATA[oai:zbc.uz.zgora.pl:publication:55128]]></dc:relation>
<dc:rights xml:lang="pl"><![CDATA[Biblioteka Uniwersytetu Zielonogórskiego]]></dc:rights>
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